Transforming Dermatological Diagnosis: Deep Learning Approaches for Skin Disease Detection in the Digital Era

Recent years have seen a significant increase in interest in dermatological pictures for the detection and identification of skin diseases due to the rising prevalence of skin disorders. This study compares three widely used deep learning models—VGG-16, ResNet50, and Inception V3—for the identification of three common skin conditions: herpes, atopic dermatitis, and acne. Using a dataset of dermatological image data, the study assesses how well each model predicts skin diseases. The performance of each model was assessed based on training duration and computational efficacy. The results show that the VGG-16 and ResNet50 models are computationally efficient, but the Inception V3 model takes longer to train because of its complex architecture. From the performed research, these findings could be utilized as the best algorithm for picture identification in dermatology. Although the comparison analysis of models is a strength of each of them, the model selection is possible only with a view to making better informed decision. Dermatologists and researchers participating in this study will have this as an enlightening addition to the stockpile of information resources. This will help to make it possible for the development of faster models for image processing that can be used to comprehend skin conditions.